Mayu Urata

Associate Professor, Graduate School of Informatics, Nagoya University *Profile is at the time of the award.

2025Inamori Research GrantsBiology & Life sciences

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Research topics: Overtourism indicators based on the collection of subjective information from local residents and tourists in the World Heritage Site Shirakawa-go

Keyword

Summary: This study aims to develop an "overtourism index" by constructing a system that collects residents' and tourists' perceptions of the number of visitors in Shirakawa-go, a UNESCO World Heritage site. The system allows residents and tourists to register their subjective perceptions of overtourism via smartphones over a certain period. By comparing this data with the estimated number of tourists and parked vehicles collected in the village, we will develop an index that aligns with the perceptions of residents and tourists, identifying the threshold at which the number of visitors exceeds capacity.

Comment

As the need for sustainable development of tourist destinations grows, the issue of overtourism is becoming increasingly severe worldwide. This study aims to integrate subjective perceptions of residents and tourists with quantitative data, such as visitor numbers and parking availability, to develop a new indicator.
By building a data collection system utilizing LINE and creating indicators that reflect local conditions, we seek to visualize the acceptable tourist capacity in Shirakawa-go and facilitate concrete policy discussions.
This research will strengthen collaboration with Shirakawa Village and local residents, improving the accuracy of the indicators through empirical experiments. To contribute to the sustainable future of tourist destinations, we will promote practical research with a focus on social implementation.

Outline of Research Achievements

This study develops a framework for addressing overtourism in Shirakawa-go by continuously collecting, integrating, and analyzing subjective data on perceived crowding from residents and tourists, together with objective data such as estimated visitor numbers based on parking usage. By establishing a system for the continuous collection of subjective data and examining its relationship with visitor volume, the study identifies the “optimal” number of visitors and thresholds of perceived congestion. These findings demonstrate the potential of data-driven tourism management that balances residents’ quality of life with visitor experience.

Bian, M., et al. (2025) AI-based license plate recognition for tourist dynamics analysis and sustainable tourism management in Shirakawa-go Journal of Global Tourism Research Vol.10, No.2, pp.129–138 https://doi.org/10.37020/jgtr.10.2_129

Okagawa, R., et al. (2025) Study on the behavior and awareness of international tourists visiting Shirakawa-go Journal of Global Tourism Research Vol.10, No.2, pp.159–164 https://doi.org/10.37020/jgtr.10.2_159

Bian, M., et al. (2025) License Plate Recognition Using Deep Learning to Manage Overtourism: A Case Study of Shirakawa-go Parking Areas IEEE 14th Global Conference on Consumer Electronics (GCCE) pp.1203–1207 DOI: 10.1109/GCCE65946.2025.11274748

Okagawa, R., et al. (2025) AI-Driven Human Mobility Monitoring Through Camera Footage in Shirakawa-go: Toward Sustainable Management of a World Heritage Site IEEE 14th Global Conference on Consumer Electronics (GCCE) pp.1036–1040 DOI: 10.1109/GCCE65946.2025.11275138